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Particle Filters for Markov Switching Stochastic Volatility Models




This paper proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. We proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method which demonstrated that we are able to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented to analyze a real time series: the foreign exchange rate of Australian dollars vs South Korean won.

Suggested Citation

  • Yun Bao & Carl Chiarella & Boda Kang, 2012. "Particle Filters for Markov Switching Stochastic Volatility Models," Research Paper Series 299, Quantitative Finance Research Centre, University of Technology, Sydney.
  • Handle: RePEc:uts:rpaper:299

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    References listed on IDEAS

    1. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
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    1. repec:eee:csdana:v:114:y:2017:i:c:p:38-46 is not listed on IDEAS

    More about this item


    Particle filters; Markov switching stochastic volatility models; Sequential Monte Carlo simulation;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory

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